Summary of Dying Clusters Is All You Need — Deep Clustering with An Unknown Number Of Clusters, by Collin Leiber et al.
Dying Clusters Is All You Need – Deep Clustering With an Unknown Number of Clusters
by Collin Leiber, Niklas Strauß, Matthias Schubert, Thomas Seidl
First submitted to arxiv on: 12 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes UNSEEN, a general framework for estimating the number of clusters in high-dimensional data without labeled data. The approach starts with a given upper bound and can be combined with various deep clustering algorithms, such as DCN, DEC, and DKM. The authors demonstrate the effectiveness of their method through an extensive experimental evaluation on several image and tabular datasets. Additionally, they perform numerous ablations to analyze their approach and show the importance of its components. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers try to find groups in pictures or text without knowing how many groups there are. They call these groups “clusters.” Right now, most methods for finding clusters require you to know how many clusters there are before starting. The authors propose a new method called UNSEEN that can figure out the number of clusters on its own. They test their method with different algorithms and show it works well on various datasets. |
Keywords
» Artificial intelligence » Clustering
